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. 2022 Jun 21;6(1):e31230.
doi: 10.2196/31230.

Continuous mHealth Patch Monitoring for the Algorithm-Based Detection of Atrial Fibrillation: Feasibility and Diagnostic Accuracy Study

Onni E Santala  1   2 , Jukka A Lipponen  3 , Helena Jäntti  4 , Tuomas T Rissanen  5 , Mika P Tarvainen  3   6 , Tomi P Laitinen  1   6 , Tiina M Laitinen  6 , Maaret Castrén  7 , Eemu-Samuli Väliaho  1   2 , Olli A Rantula  1   2 , Noora S Naukkarinen  1   2 , Juha E K Hartikainen  1   8 , Jari Halonen #  1   8 , Tero J Martikainen #  9
Affiliations

Continuous mHealth Patch Monitoring for the Algorithm-Based Detection of Atrial Fibrillation: Feasibility and Diagnostic Accuracy Study

Onni E Santala et al. JMIR Cardio. .

Abstract

Background: The detection of atrial fibrillation (AF) is a major clinical challenge as AF is often paroxysmal and asymptomatic. Novel mobile health (mHealth) technologies could provide a cost-effective and reliable solution for AF screening. However, many of these techniques have not been clinically validated.

Objective: The purpose of this study is to evaluate the feasibility and reliability of artificial intelligence (AI) arrhythmia analysis for AF detection with an mHealth patch device designed for personal well-being.

Methods: Patients (N=178) with an AF (n=79, 44%) or sinus rhythm (n=99, 56%) were recruited from the emergency care department. A single-lead, 24-hour, electrocardiogram-based heart rate variability (HRV) measurement was recorded with the mHealth patch device and analyzed with a novel AI arrhythmia analysis software. Simultaneously registered 3-lead electrocardiograms (Holter) served as the gold standard for the final rhythm diagnostics.

Results: Of the HRV data produced by the single-lead mHealth patch, 81.5% (3099/3802 hours) were interpretable, and the subject-based median for interpretable HRV data was 99% (25th percentile=77% and 75th percentile=100%). The AI arrhythmia detection algorithm detected AF correctly in all patients in the AF group and suggested the presence of AF in 5 patients in the control group, resulting in a subject-based AF detection accuracy of 97.2%, a sensitivity of 100%, and a specificity of 94.9%. The time-based AF detection accuracy, sensitivity, and specificity of the AI arrhythmia detection algorithm were 98.7%, 99.6%, and 98.0%, respectively.

Conclusions: The 24-hour HRV monitoring by the mHealth patch device enabled accurate automatic AF detection. Thus, the wearable mHealth patch device with AI arrhythmia analysis is a novel method for AF screening.

Trial registration: ClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335.

Keywords: Awario analysis Service, screening; HRV; algorithm; arrhythmia; artificial intelligence; atrial fibrillation; feasibility; heart rate; heart rate variability; mHealth; mobile health; mobile patch; reliability; risk; screening; stroke; stroke risk; wearable.

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Conflict of interest statement

Conflicts of Interest: OES, JAL, TTR, TJM, HJ, JH, ESV, and MPT are shareholders of a company (Heart2Save) that designs electrocardiogram-based software for medical equipment. JAL, MPT, and HJ report personal fees from Heart2Save.

Figures

Figure 1
Figure 1
Heart rate variability (HRV) and electrocardiogram (ECG) recordings. Single-lead ECG-based HRV recording (1) and 3-lead Holter ECG recording (2). LA: left arm; LL: left limb; RA: right arm; V3: V3 lead in 12-lead ECG.
Figure 2
Figure 2
Study flow chart. AF: atrial fibrillation; HRV: heart rate variability.
Figure 3
Figure 3
Percentage of interpretable heart rate variability data in individual subject recordings. Subjects sorted by using automatic quality values.
See this image and copyright information in PMC

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